We also compared how many reviews, size \(k\) , should be present on the input side. While there were no strong patterns as for sampling methods, typically more reviews, eg. 160 performed better most of the time.
Next we compare how well the models perform in the unsupervised summarization
setting. Here we train our models on the silver data and evaluate on the test set of SPACE-eval.
Table REF  presents
the ROUGE scores. We compare with previous Lexrank [1]} results as well as the current best system ACESUM by [2]}.
